Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_plot
beta_std plot
beta_std_plot
Metadata
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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_4105.vcf.gz --id UKB-b:1096 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4105.txt.gz --cohort_controls 146139 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-13T02:30:29.639892",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-1096/ukb-b-1096.vcf.gz; Date=Sun May 10 07:16:42 2020"
}
LDSC
*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1096/UKB-b-1096_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1096/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1096/UKB-b-1096_data.vcf.gz ...
Read summary statistics for 9301601 SNPs.
Dropped 10285 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1287946 SNPs remain.
After merging with regression SNP LD, 1287946 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2893 (0.0259)
Lambda GC: 1.4224
Mean Chi^2: 1.9591
Intercept: 1.095 (0.0137)
Ratio: 0.0991 (0.0143)
Analysis finished at Thu Oct 17 14:42:00 2019
Total time elapsed: 1.0m:42.2sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9487,
"inflation_factor": 1.3107,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 215,
"n_p_sig": 26574,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 111623,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NaN",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1287946,
"ldsc_nsnp_merge_regression_ld": 1287946,
"ldsc_observed_scale_h2_beta": 0.2893,
"ldsc_observed_scale_h2_se": 0.0259,
"ldsc_intercept_beta": 1.095,
"ldsc_intercept_se": 0.0137,
"ldsc_lambda_gc": 1.4224,
"ldsc_mean_chisq": 1.9591,
"ldsc_ratio": 0.0991
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | FALSE |
| ldsc_intercept_beta | FALSE |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
Definitions
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations for<*>column.
se_n metrics
n_est: Estimated sample size value, \(\widehat{n}\).ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expectratio_se_nto be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.- \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
- \({z}_j = \frac{\beta_j}{{se}_j}\),
- and \(\beta_j\) is the reported effect size.
ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean ofdiffand the mean ofdiff2(expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.- \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
- \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.- \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
- \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
- and \({se}_j\) is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.- \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
- \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),
r2 metrics
Sum of variance explained, calculated from the clumped top hits sample.
r2_sum<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).
LDSC metrics
Metrics from LD regression
ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.ldsc_observed_scale_h2_{beta,se}Coefficient value and SE for total observed scale h2.ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.ldsc_lambda_gc: Lambda GC statistics.ldsc_mean_chisq: Mean \(\chi^2\) statistics.ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).
Flags
When a metric needs attention, the flag should return TRUE.
af_correlation:abs(af_correlation)< 0.7.inflation_factor:inflation_factor> 1.2.n:n(max reported sample size) < 10000.is_snpid_non_unique: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_sum<*>:r2_sum<*>> 0.5
Plots
- Manhattan plot
- Red line: \(-log_{10}^{5 \times 10^{-8}}\)
- Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
- QQ plot
- AF plot
- P-Z plot
- beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)
Diagnostics
Details
Summary stats
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 3 | 58 | 0 | 9291368 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 9301601 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.635461e+00 | 5.754217e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.880964e+07 | 5.630840e+07 | 828.0000000 | 3.250009e+07 | 6.939206e+07 | 1.145402e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.140000e-05 | 1.375080e-02 | -0.2588190 | -5.333200e-03 | 1.610000e-05 | 5.363200e-03 | 3.950820e-01 | ▁▇▅▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.913100e-03 | 7.813100e-03 | 0.0033239 | 4.006200e-03 | 6.325300e-03 | 1.359080e-02 | 8.354940e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.552220e-01 | 3.019024e-01 | 0.0000000 | 1.800002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.552242e-01 | 3.018779e-01 | 0.0000000 | 1.827896e-01 | 4.400233e-01 | 7.169223e-01 | 9.999999e-01 | ▇▆▆▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.138876e-01 | 2.578300e-01 | 0.0023950 | 1.754700e-02 | 9.267400e-02 | 3.358640e-01 | 9.976050e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 111623 | 0.9879996 | NA | NA | NA | NA | NA | NA | NA | 2.148829e-01 | 2.496223e-01 | 0.0000000 | 1.477640e-02 | 1.110220e-01 | 3.356630e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0017213 | 0.0061325 | 0.7800007 | 0.7789498 | 0.623774 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0053527 | 0.0060889 | 0.3800004 | 0.3793505 | 0.399295 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0006321 | 0.0097000 | 0.9500000 | 0.9480393 | 0.103821 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0052635 | 0.0059988 | 0.3800004 | 0.3802543 | 0.456070 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0050517 | 0.0118546 | 0.6700003 | 0.6700076 | 0.074424 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0017778 | 0.0068514 | 0.8000000 | 0.7952651 | 0.241234 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0283112 | 0.0085072 | 0.0008800 | 0.0008750 | 0.913035 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0008276 | 0.0057209 | 0.8800001 | 0.8849830 | 0.116842 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0189149 | 0.0083514 | 0.0239999 | 0.0235197 | 0.067730 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0022052 | 0.0042338 | 0.5999997 | 0.6024697 | 0.515118 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0068701 | 0.0051497 | 0.1800002 | 0.1821731 | 0.137094 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0030284 | 0.0066957 | 0.6499995 | 0.6510599 | 0.072767 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0052903 | 0.0089394 | 0.5500004 | 0.5539873 | 0.041645 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0030306 | 0.0089574 | 0.7400005 | 0.7351086 | 0.049132 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0032354 | 0.0067008 | 0.6300007 | 0.6292061 | 0.072246 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0058058 | 0.0078488 | 0.4600002 | 0.4594807 | 0.054407 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0063272 | 0.0078896 | 0.4199997 | 0.4225772 | 0.054014 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0035324 | 0.0067250 | 0.5999997 | 0.5993949 | 0.072117 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0083271 | 0.0157577 | 0.5999997 | 0.5971904 | 0.020157 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0061080 | 0.0040894 | 0.1400000 | 0.1352762 | 0.297636 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623774 ES:SE:LP:AF:ID 0.00172131:0.00613247:0.107905:0.623774:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399295 ES:SE:LP:AF:ID -0.00535274:0.00608893:0.420216:0.399295:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103821 ES:SE:LP:AF:ID 0.000632141:0.0097:0.0222764:0.103821:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45607 ES:SE:LP:AF:ID 0.00526354:0.00599883:0.420216:0.45607:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074424 ES:SE:LP:AF:ID 0.00505169:0.0118546:0.173925:0.074424:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241234 ES:SE:LP:AF:ID -0.0017778:0.0068514:0.09691:0.241234:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913035 ES:SE:LP:AF:ID 0.0283112:0.0085072:3.05552:0.913035:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116842 ES:SE:LP:AF:ID -0.000827562:0.00572092:0.0555173:0.116842:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06773 ES:SE:LP:AF:ID -0.0189149:0.00835137:1.61979:0.06773:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515118 ES:SE:LP:AF:ID -0.0022052:0.00423383:0.221849:0.515118:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03348 ES:SE:LP:AF:ID 0.00579093:0.0105909:0.236572:0.03348:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037189 ES:SE:LP:AF:ID 0.00463775:0.00961464:0.200659:0.037189:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037314 ES:SE:LP:AF:ID 0.0043892:0.00957718:0.187087:0.037314:rs12184277
1 717485 rs12184279 C A . PASS AF=0.03697 ES:SE:LP:AF:ID 0.00286912:0.00965006:0.113509:0.03697:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016334 ES:SE:LP:AF:ID -0.00574072:0.0150568:0.154902:0.016334:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037548 ES:SE:LP:AF:ID 0.00409981:0.00953972:0.173925:0.037548:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037639 ES:SE:LP:AF:ID 0.00383015:0.00951001:0.161151:0.037639:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101571 ES:SE:LP:AF:ID 0.0105481:0.00697444:0.886057:0.101571:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958417 ES:SE:LP:AF:ID -0.00717711:0.00917566:0.366532:0.958417:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031774 ES:SE:LP:AF:ID -0.0157016:0.0167122:0.455932:0.031774:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052678 ES:SE:LP:AF:ID -0.0109234:0.013529:0.376751:0.052678:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037123 ES:SE:LP:AF:ID 0.00521276:0.00957468:0.229148:0.037123:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037433 ES:SE:LP:AF:ID 0.00342068:0.00949156:0.142668:0.037433:rs116720794
1 729679 rs4951859 C G . PASS AF=0.84216 ES:SE:LP:AF:ID -0.00028562:0.00495176:0.0222764:0.84216:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056058 ES:SE:LP:AF:ID -0.00711216:0.00803986:0.420216:0.056058:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122758 ES:SE:LP:AF:ID -0.000748195:0.00542987:0.05061:0.122758:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025678 ES:SE:LP:AF:ID -0.0050883:0.0133667:0.154902:0.025678:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121981 ES:SE:LP:AF:ID -0.00130167:0.00543254:0.091515:0.121981:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133218 ES:SE:LP:AF:ID -0.000729628:0.0053435:0.05061:0.133218:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011242 ES:SE:LP:AF:ID 0.00280731:0.0193574:0.0555173:0.011242:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005842 ES:SE:LP:AF:ID 0.0295568:0.0247385:0.638272:0.005842:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037379 ES:SE:LP:AF:ID 0.00319021:0.00939432:0.136677:0.037379:rs146277091
1 752566 rs3094315 G A . PASS AF=0.8378 ES:SE:LP:AF:ID -0.00191529:0.00479242:0.161151:0.8378:rs3094315
1 752721 rs3131972 A G . PASS AF=0.837418 ES:SE:LP:AF:ID -0.0020032:0.00478689:0.167491:0.837418:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869055 ES:SE:LP:AF:ID -0.00273636:0.00513868:0.229148:0.869055:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130639 ES:SE:LP:AF:ID 0.00270444:0.00514887:0.221849:0.130639:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037874 ES:SE:LP:AF:ID 0.00541113:0.00924016:0.251812:0.037874:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038132 ES:SE:LP:AF:ID 0.00588879:0.0091808:0.283997:0.038132:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868392 ES:SE:LP:AF:ID -0.00296164:0.00512842:0.251812:0.868392:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868491 ES:SE:LP:AF:ID -0.0030022:0.00513037:0.251812:0.868491:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038056 ES:SE:LP:AF:ID 0.00557109:0.00922163:0.259637:0.038056:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868384 ES:SE:LP:AF:ID -0.00293611:0.00512808:0.244125:0.868384:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005174 ES:SE:LP:AF:ID 0.031085:0.0262751:0.619789:0.005174:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005146 ES:SE:LP:AF:ID 0.0308318:0.0263309:0.619789:0.005146:rs142682604
1 754503 rs3115859 G A . PASS AF=0.836911 ES:SE:LP:AF:ID -0.00235877:0.00477466:0.207608:0.836911:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038066 ES:SE:LP:AF:ID 0.00573778:0.00923419:0.275724:0.038066:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83754 ES:SE:LP:AF:ID -0.00204504:0.00478799:0.173925:0.83754:rs3131966
1 755240 rs181660517 T G . PASS AF=0.01325 ES:SE:LP:AF:ID 0.00993872:0.0171209:0.251812:0.01325:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005485 ES:SE:LP:AF:ID -0.0200891:0.0259502:0.356547:0.005485:rs184270342
1 755775 rs3131965 A G . PASS AF=0.838771 ES:SE:LP:AF:ID -0.00202008:0.00485394:0.167491:0.838771:rs3131965